Furthermore, we utilize the mixture models technique in our framework to train the NMT predictor, where multiple expert models are trained and combined.
In particular, each expert model is assigned with a different start-of-sentence token, and is trained with the EM algorithm [1]}, so that different expert models can specialize on different translation styles.
Therefore, our NMT predictor is capable of producing diverse translation outputs, thus accommodating the one-to-many setup of machine translation.
